{"id":"W2951956812","doi":"10.1101/077990","title":"Variant Set Enrichment: An R package to Identify Dis-ease-Associated Functional Genomic Regions","year":2016,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre; Ontario Institute for Cancer Research; University of Toronto; University Health Network","funders":"Princess Margaret Cancer Foundation; Natural Sciences and Engineering Research Council of Canada; Canadian Cancer Society Research Institute; Canadian Institutes of Health Research; National Cancer Institute; National Institutes of Health","keywords":"Set (abstract data type); Genome; Computational biology; Biology; Disease; Human genome; R package; Computer science; Genetics; Gene; Medicine","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007849573,0.003495123,0.003334727,0.005175032,0.001078047,0.003024011,0.004260995,0.001330395,0.05297747],"category_scores_gemma":[0.044352,0.002023786,0.004366352,0.004440672,0.001085187,0.001455908,0.003876617,0.002867832,0.02391073],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005403825,"about_ca_system_score_gemma":0.002869255,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003266277,"about_ca_topic_score_gemma":0.003607209,"domain_scores_codex":[0.9944555,0.002516464,0.0004300429,0.001376591,0.0009188761,0.0003024854],"domain_scores_gemma":[0.974944,0.02029553,0.001129457,0.002415142,0.0007599734,0.0004558975],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.002596745,0.0002138045,0.05031455,0.007550649,0.01303296,0.003499493,0.001142474,0.02832691,0.01203149,0.02723636,0.6708477,0.1832069],"study_design_scores_gemma":[0.002782179,0.0006176054,0.05675198,0.001212002,0.007203637,0.006404518,0.0004350746,0.2022584,0.02315576,0.1465699,0.5517614,0.0008475279],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"software","genre_scores_codex":[0.02173998,0.002453935,0.5563769,0.001359823,0.0008035532,0.0006479088,0.2069882,0.2033025,0.006327138],"genre_scores_gemma":[0.1374026,0.001428549,0.628341,0.001194073,0.0004078487,0.003648066,0.1325116,0.08871894,0.006347443],"genre_candidate":"software","genre_consensus":null,"teacher_disagreement_score":0.05297747,"threshold_uncertainty_score":0.1772273,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02140370413200629,"score_gpt":0.25863183758507,"score_spread":0.2372281334530637,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}